Weather-based Pest Forecasting for Efficient Crop Protection

Olatinwo, Rabiu; Hoogenboom, Gerrit

doi:10.1016/b978-0-12-398529-3.00005-1

Cited by 24 publications

(15 citation statements)

References 58 publications

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“…Temperature and humidity conditions can be recorded in the vineyard by using calibrated weather sensors that provide the data to disease prediction models ( Sanna et al, 2018 ); the use of weather forecasts in predictive systems for disease management is also common ( Bourke, 1970 ; Gent et al, 2013 ; Olatinwo and Hoogenboom, 2014 ), thanks to a progressive increase in their accuracy ( Bauer et al, 2015 ) and to the availability of methods for refining forecast data using point observations ( Leblanc, 2019 ). A mechanistic, weather-driven model for accurate prediction of B. cinerea infection risk in vineyards has been developed and validated ( González-Domínguez et al, 2015 ; Fedele et al, 2020b ).…”

Section: Discussionmentioning

confidence: 99%

Biocontrol of Botrytis cinerea on Grape Berries as Influenced by Temperature and Humidity

2020

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Six commercial biocontrol agents (BCAs, containing Aureobasidium pullulans, Bacillus amyloliquefaciens, Bacillus amyloliquefaciens plantarum, Bacillus subtilis, Pythium oligandrum, or Trichoderma atroviride) were applied to ripening berries that were then incubated at one of four temperatures (T, 15, 20, 25, and 30°C) and one of four relative humidity levels (RH, 60, 80, 90, and 100%). After 1 to 13 days of incubation (BCA colonization period), the berries were inoculated with conidia of Botrytis cinerea and kept at 25°C and 100% RH for 7 days, at which time Botrytis bunch rot (BBR) was assessed. The response of BBR control to T/RH conditions and BCA colonization period differed among BCAs; the coefficients of variation among the BCAs ranged from 44.7 to 72.4%. An equation was developed that accounted for the combined effects of T, RH, and BCA colonization period on BBR control. The equation, which had an R 2 >0.94, could help farmers select the BCA to be used for a specific application based on weather conditions at the time of treatment and in the following days.

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Section: Discussionmentioning

confidence: 99%

Biocontrol of Botrytis cinerea on Grape Berries as Influenced by Temperature and Humidity

2020

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“…For 15 pest species, we added temperature seasonality to the explanatory variables and used a subset of the five variables to reach better model performance. All explanatory variables were chosen based on ecological significance 68,69 . We chose the variables based on ecological importance rather than statistical information criteria 70 (see Supplementary Data 1 and 2 for the variables included for each species).…”

Section: Methodsmentioning

confidence: 99%

Crop and forest pest metawebs shift towards increased linkage and suitability overlap under climate change

et al. 2020

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Global changes pose both risks and opportunities to agriculture and forestry, and biological forecasts can inform future management strategies. Here, we investigate potential land-use opportunities arising from climate change for these sectors in Europe, and risks associated with the introduction and establishment of novel insect pests. Adopting a metaweb approach including all interaction links between 126 crops and forest tree species and 89 black-listed insect pest species, we show that the metawebs shift toward increased numbers of links and overlap of suitable area under climate change. Decomposing the metaweb across regions shows large saturation in southern Europe, while many novel interactions are expected for northern Europe. In light of the rising consumer awareness about human health and environmental impacts of food and wood production, the challenge will be to effectively exploit new opportunities to create diverse local agriculture and forestry while controlling pest species and reducing risks from pesticide use.

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“…septoria leaf blotch of wheat [69]. Our models can be easily updated with new observational data, providing an additional level of confidence to end users in terms of model accuracy [70].…”

Section: Plos Onementioning

confidence: 98%

Forecasting severe grape downy mildew attacks using machine learning

Chen

Brun²,

Raynal³

et al. 2020

PLoS ONE

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Grape downy mildew (GDM) is a major disease of grapevine that has an impact on both the yields of the vines and the quality of the harvested fruits. The disease is currently controlled by repetitive fungicide treatments throughout the season, especially in the Bordeaux vineyards where the average number of fungicide treatments against GDM was equal to 10.1 in 2013. Reducing the number of treatments is a major issue from both an environmental and a public health point of view. One solution would be to identify vineyards that are likely to be heavily attacked in spring and then apply fungicidal treatments only to these situations. In this perspective, we use here a dataset including 9 years of GDM observations to develop and compare several generalized linear models and machine learning algorithms predicting the probability of high incidence and severity in the Bordeaux region. The algorithms tested use the date of disease onset and/or average monthly temperatures and precipitation as input variables. The accuracy of the tested models and algorithms is assessed by year-byyear cross validation. LASSO, random forest and gradient boosting algorithms show better performance than generalized linear models. The date of onset of the disease has a greater influence on the accuracy of forecasts than weather inputs and, among weather inputs, precipitation has a greater influence than temperature. The best performing algorithm was selected to evaluate the impact of contrasted climate scenarios on GDM risk levels. Results show that risk of GDM at bunch closure decreases with reduced rainfall and increased temperatures in April-May. Our results also show that the use of fungicide treatment decision rules that take into account local characteristics would reduce the number of treatments against GDM in the Bordeaux vineyards compared to current practices by at least 50%.

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Weather-based Pest Forecasting for Efficient Crop Protection

Cited by 24 publications

References 58 publications

Biocontrol of Botrytis cinerea on Grape Berries as Influenced by Temperature and Humidity

Biocontrol of Botrytis cinerea on Grape Berries as Influenced by Temperature and Humidity

Crop and forest pest metawebs shift towards increased linkage and suitability overlap under climate change

Forecasting severe grape downy mildew attacks using machine learning

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